Examples
Ben Bolker
Includes material from Ian Dworkin and Jonathan Dushoff, but they bear no responsibility for the contents.
Simplified Bayes workflow
- Decide on model specification; journal it, along with justifications
- Decide on priors, including prior predictive simulations
- Data exploration (graphical)
- Fit model
- MCMC diagnostics
- Model diagnostics, including comparing posterior predictive sims to data
- Interpret/predict/etc.
Examples
‘sleepstudy’ data
From ?lme4::sleepstudy, “The average reaction time per day (in milliseconds) for subjects in a sleep deprivation study.” A standard example for mixed models.
library(lme4)
library(brms)
options(brms.backend = "cmdstanr")
library(broom.mixed)
library(purrr)
library(dplyr)
library(tidybayes)
library(bayesplot)
library(bayestestR)
library(ggplot2); theme_set(theme_bw())
library(ggrastr)
library(cowplot)We’ll fit the model Reaction ~ Days + (Days|Subject) (a linear regression + random-slopes model)
\[ \newcommand{\XX}{\mathbf X} \newcommand{\ZZ}{\mathbf Z} \newcommand{\bbeta}{\boldsymbol \beta} \newcommand{\bb}{\mathbf b} \newcommand{\zero}{\boldsymbol 0} \begin{split} \textrm{Reaction} & \sim \textrm{Normal}(\XX \bbeta + \ZZ \bb, \sigma^2) \\ \bb & \sim \textrm{MVNorm}(\zero, \Sigma) \\ \Sigma & = \textrm{block-diag}, \left( \begin{array}{cc} \sigma^2_i & \sigma_{is} \\ \sigma_{is} & \sigma^2_{s} \end{array} \right) \end{split} \]
form1 <- Reaction ~ Days + (Days|Subject)prior predictive simulation
brms has a convenience function get_prior() that lays out the parameters/priors that need to be set, and their default values …
get_prior(form1, sleepstudy)## prior class coef group resp dpar nlpar lb ub source
## (flat) b default
## (flat) b Days (vectorized)
## lkj(1) cor default
## lkj(1) cor Subject (vectorized)
## student_t(3, 288.7, 59.3) Intercept default
## student_t(3, 0, 59.3) sd 0 default
## student_t(3, 0, 59.3) sd Subject 0 (vectorized)
## student_t(3, 0, 59.3) sd Days Subject 0 (vectorized)
## student_t(3, 0, 59.3) sd Intercept Subject 0 (vectorized)
## student_t(3, 0, 59.3) sigma 0 defaultInfo on brms default priors: Intercept is Student \(t\) with 3 df, mean equal to observed mean (median??), SD equal to (rescaled) MAD. RE SDs are the same but half-\(t\) (see lb = 0 column), mode at 0.
Constraining intercept and slope to ‘reasonable’ values:
b_prior <- c(set_prior("normal(200, 50)", "Intercept"),
set_prior("normal(0, 10)", "b")
)Helper function for prior predictive simulations: run a short MCMC chain, plot results. (We’re going to ignore/suppress warnings for this stage …)
test_prior <- function(p) {
## https://discourse.mc-stan.org/t/suppress-all-output-from-brms-in-markdown-files/30117/3
capture.output(
b <- brm(form1, sleepstudy, prior = p,
seed = 101, ## reproducibility
sample_prior = 'only', ## for prior predictive sim
chains = 1, iter = 500, ## very short sample for convenience
silent = 2, refresh = 0 ## be vewy vewy quiet ...
)
)
p_df <- sleepstudy |> add_predicted_draws(b)
## 'spaghetti plot' of prior preds
gg0 <- ggplot(p_df,aes(Days, .prediction, group=interaction(Subject,.draw))) +
geom_line(alpha = 0.1)
print(gg0)
invisible(b) ## return without printing
}
test_prior(b_prior)
Decrease random-effects SDs:
b_prior2 <- c(set_prior("normal(200, 10)", "Intercept"),
set_prior("normal(0, 5)", "b"),
set_prior("student_t(3, 0, 0.1)", "sd")
)
test_prior(b_prior2)
(??why is this not noisy due to large sigma??)
Make all scales even smaller?
b_prior3 <- c(set_prior("normal(200, 5)", "Intercept"),
set_prior("normal(0, 2)", "b"),
set_prior("student_t(3, 0, 0.01)", "sd")
)
test_prior(b_prior3)
We forgot to constrain the prior for the residual standard deviation!
b_prior4 <- c(set_prior("normal(200, 5)", "Intercept"),
set_prior("normal(0, 2)", "b"),
set_prior("normal(0, 1)", "sd"),
set_prior("normal(0, 1)", "sigma")
)
test_prior(b_prior4)
Now relax a bit …
b_prior5 <- c(set_prior("normal(200, 10)", "Intercept"),
set_prior("normal(0, 8)", "b"),
set_prior("student_t(10, 0, 3)", "sd"),
set_prior("student_t(10, 0, 3)", "sigma")
)
test_prior(b_prior5)
In hindsight, should relax more. Set intercept back to default value, widen fixed-effect (slope) prior
b_prior6 <- c(set_prior("normal(0, 20)", "b"),
set_prior("student_t(10, 0, 3)", "sd"),
set_prior("student_t(10, 0, 3)", "sigma")
)
test_prior(b_prior6)
fitting
m_lmer <- lmer(form1, sleepstudy)b_reg <- brm(form1, sleepstudy, prior = b_prior5,
seed = 101, ## reproducibility
## handle divergence
control = list(adapt_delta = 0.95)
)
b_default <- brm(form1, sleepstudy,
seed = 101, ## reproducibility
)load("examples1.rda")diagnose
print(bayestestR::diagnostic_posterior(b_reg),
digits = 4)## Parameter Rhat ESS MCSE
## 1 b_Days 1.001 1185 0.08135
## 2 b_Intercept 1.002 1152 0.28076summary(b_reg)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: Reaction ~ Days + (Days | Subject)
## Data: sleepstudy (Number of observations: 180)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~Subject (Number of levels: 18)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 20.69 7.76 6.17 37.31 1.00 865 577
## sd(Days) 10.89 2.68 6.51 16.76 1.00 1212 2037
## cor(Intercept,Days) 0.50 0.25 -0.04 0.91 1.00 703 786
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 233.85 9.53 213.16 250.81 1.00 1064 934
## Days -0.42 2.80 -5.97 4.76 1.00 1224 1832
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 25.69 1.63 22.87 29.21 1.00 2231 2618
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).\(\hat R\) (Rhat), ESS look OK. Vehtari et al. (2021) recommend an \(\hat R\) threshold of 1.01, ESS > 400, MCSE (Monte Carlo standard error) ‘small enough’
figure out what is the needed accuracy for the quantity of interest (for reporting usually 2 significant digits is enough
regex_pars specifies a regular expression for deciding which parameters to show
mcmc_trace(b_reg, regex_pars= "b_|sd_")
Vehtari et al. (2021) recommend rank-histograms instead: chains should have similar rank distributions
mcmc_rank_overlay(b_reg, regex_pars= "b_|sd_")
Plot results. broom.mixed::tidy() will get what you need - complicated code below is to get everything lined up nicely.
brms_modlist <- list(brms_default = b_default, brms_reg = b_reg, brms_reg2 = b_reg2)
res_bayes <- (brms_modlist
|> purrr::map_dfr(~ tidy(., conf.int = TRUE), .id = "model")
)
## need to do separately - different conf.method choices
res_lmer <- (m_lmer
|> tidy(conf.int = TRUE, conf.method = "profile")
|> mutate(model = "lmer", .before = 1)
)## Computing profile confidence intervals ...
## Computing profile confidence intervals ...res <- (bind_rows(res_lmer, res_bayes)
|> select(-c(std.error, statistic, component, group))
|> filter(term != "(Intercept)")
|> mutate(facet = ifelse(grepl("^cor", term), "cor",
ifelse(grepl("Days", term), "Days",
"int")))
)ggplot(res, aes(estimate, term, colour = model, shape = model)) +
geom_pointrange(aes(xmin = conf.low, xmax = conf.high),
position = position_dodge(width = 0.5)) +
facet_wrap(~ facet, scale = "free",ncol = 1) +
guides(colour = guide_legend(reverse=TRUE),
shape = guide_legend(reverse=TRUE))
posterior predictive simulations, compare with data
post_df1 <- sleepstudy |> add_predicted_draws(b_reg)
gg1 <- ggplot(post_df1,
aes(Days, .prediction, group=interaction(Subject,.draw))) +
rasterise(geom_line(alpha = 0.1)) +
geom_point(aes(y=Reaction), col = "red")
print(gg1)
post_df2 <- sleepstudy |> add_predicted_draws(b_reg2)
print(gg1 %+% post_df2)
post_df1B <- filter(post_df1, Subject == levels(Subject)[1])
post_df2B <- filter(post_df2, Subject == levels(Subject)[1])
plot_grid(gg1 %+% post_df1B, gg1 %+% post_df2B)
list(bad = b_reg, good = b_reg2) |>
purrr::map_dfr(tidy, effects = "fixed", .id = "priors")## # A tibble: 4 × 8
## priors effect component term estimate std.error conf.low conf.high
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 bad fixed cond (Intercept) 234. 9.53 213. 251.
## 2 bad fixed cond Days -0.418 2.80 -5.97 4.76
## 3 good fixed cond (Intercept) 251. 4.88 242. 261.
## 4 good fixed cond Days 10.4 1.50 7.55 13.5¿¿¿¿ I can see that b_prior5/b_reg give “wrong” answers (priors are too tight), but I would like to be able to diagnose that from the posterior predictive plots. I can’t see any difference between these and the b_prior6/b_reg2 PostPD plots! What am I missing ???
Plot random effect draws, from here:
Random effects (deviations from population-level/fixed-effect predictions):
brms_modlist <- list(brms_default = b_default, brms_reg = b_reg, brms_reg2 = b_reg2)
ranefs <- (brms_modlist
|> purrr::map_dfr(~ tidy(., effects = "ran_vals", conf.int = TRUE), .id = "model")
)
gg_r <- ggplot(ranefs, aes(estimate, level, colour = model)) +
geom_pointrange(aes(xmin = conf.low, xmax = conf.high), position = position_dodge(width = 0.5)) +
facet_wrap(~term, scale = "free", nrow = 1)
print(gg_r + geom_vline(lty = 2, xintercept = 0))
Group-level coefficients (predicted intercept/slope for each subject):
## this is way too ugly - needs to be incorporated into broom.mixed as an option
my_coefs <- function(x) {
meltfun <- function(a) {
dd <- as.data.frame(ftable(a)) |>
setNames(c("level", "var", "term", "value")) |>
tidyr::pivot_wider(names_from = var, values_from = value) |>
rename(estimate = "Estimate",
std.error = "Est.Error",
## FIXME: not robust to changing levels
conf.low = "Q2.5",
conf.high = "Q97.5")
}
purrr:::map_dfr(coef(x), meltfun, .id = "group")
}
coefs <- (brms_modlist
|> purrr::map_dfr(my_coefs, .id = "model")
)
print(gg_r %+% coefs)
check_prior(b_reg)## Parameter Prior_Quality
## 1 b_Intercept informative
## 2 b_Days informativetry(check_prior(b_reg2)) ## ugh## Error in if (stats::sd(posterior, na.rm = TRUE) > 0.1 * stats::sd(prior, :
## missing value where TRUE/FALSE neededtry(check_prior(b_reg2, method = "lakeland")) ## ugh## Parameter Prior_Quality
## 1 b_Intercept informative
## 2 b_Days informativeReferences
Vehtari, Aki, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner. 2021. “Rank-Normalization, Folding, and Localization: An Improved R-hat for Assessing Convergence of MCMC (with Discussion).” Bayesian Analysis 16 (2): 667–718. https://doi.org/10.1214/20-BA1221.
Last updated: 31 May 2023 17:15